A novel integrated method for short-term wind power forecasting based on fluctuation clustering and history matching

Lin Ye, Yilin Li, Ming Pei, Yongning Zhao, Zhuo Li and Peng Lu

Applied Energy, 2022, vol. 327, issue C, No S0306261922013885

Abstract: The intermittent and randomness of wind power poses a great challenge to the safe and stable operation of power systems. Accurate wind power forecasting is a key to reduce the negative impact of uncertainty on the entire system. To this end, a novel integrated method for short-term wind power forecasting is proposed based on frequency analysis, fluctuation clustering and history matching. Through variational modal decomposition (VMD) and Pearson correlation coefficient between wind resource and wind power, the wind power sequence is decomposed into a trend component and a fluctuation component. Then both the two components are sequentially divided into time series segments with equal lengths, which are set as the variation period determined by fast Fourier transform (FFT). Time series segments of the trend component are clustered by feature extraction and fuzzy C-means (FCM) clustering method, and accordingly the segments of fluctuation component are clustered as well. The correlation consistency between trend components and fluctuation components is further studied. Finally, the trend component is forecasted by random forest (RF) model, and the fluctuation component is forecasted by matching its counterpart trend component from historical similar segments database based on the correlation consistency. The performance and effectiveness of the proposed method are verified by using four wind farms in Jilin Province, China. Results indicate that the forecasting accuracy of the proposed method is greatly improved compared with the conventional forecasting model, which proves the validity.

Keywords: Wind power forecasting; Short-term forecasting; Periodicity analysis; Fuzzy C-mean; Ensemble learning (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261922013885
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:327:y:2022:i:c:s0306261922013885

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2022.120131

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().